Predicting Suicidal Behavior From Longitudinal Electronic Health Records is organized by American Psychiatric Association (APA) and will be held from Feb 01, 2017 - Jan 31, 2019.
This program is designed for all psychiatrists in clinical practice, residents in Graduate Medical Education programs, medical students interested in psychiatry, and other physicians who wish to advance their current knowledge of clinical medicine.
The APA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Objective: The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients’ future risk of suicidal behavior.
Method: Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998–2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N=1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set.
The participant will describe how risk screening techniques can be deployed to study electronic health records for potential risk of suicidal behavior.
Additional details will be posted as soon as they are available.